These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Type
Origin
Scope
SUBMIT A TOOL
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SubmitMITRE Atlas
TechnicalUnited StatesUploaded on May 2, 2025ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a globally accessible, living knowledge base of adversary tactics and techniques against Al-enabled systems based on real-world attack observations and realistic demonstrations from Al red teams and security groups.
Objective(s)
HiddenLayer AISec Platform
TechnicalUnited StatesUploaded on May 19, 2025HiddenLayer’s AISec Platform is a GenAI Protection Suite purpose-built to ensure the integrity of AI models throughout the MLOps pipeline. The platform provides detection and response for GenAI and traditional AI models to detect prompt injections, adversarial AI attacks, and digital supply chain vulnerabilities.
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ISO/IEC 25023:2016 - Systems and software engineering. Systems and software Quality Requirements and Evaluation (SQuaRE). Measurement of system and software product quality
ProceduralUploaded on Jan 6, 2025ISO/IEC 25023:2016 defines quality measures for quantitatively evaluating system and software product quality in terms of characteristics and subcharacteristics defined in ISO/IEC 25010 and is intended to be used together with ISO/IEC 25010.
Objective(s)
Mindgard
TechnicalUnited KingdomUploaded on Dec 6, 2024Continuous automated red teaming for AI, minimize security threats to AI models and applications.
Objective(s)
garak
TechnicalUploaded on Nov 5, 2024garak, Generative AI Red-teaming & Assessment Kit, is an LLM vulnerability scanner. Garak checks if an LLM can be made to fail.
Objective(s)
SPLX Platform: End-to-end AI Security Platform
TechnicalUnited StatesUploaded on Aug 2, 2024AI Security Platform for GenAI and Conversational AI applications. Probe enables security officers and developers identify, mitigate, and monitor AI system security.
Objective(s)
ITU-T Y.3177 - Architectural framework for artificial intelligence-based network automation for resource and fault management in future networks including IMT-2020
ProceduralUploaded on Jul 1, 2024This Recommendation specifies an architectural framework for network automation based on artificial intelligence (AI) for resource and fault management in future networks, including international mobile telecommunications-2020 (IMT-2020).
Objective(s)
ITU-T Y.3179 - Architectural framework for machine learning model serving in future networks including IMT-2020
ProceduralUploaded on Jul 1, 2024This Recommendation provides an architectural framework for machine learning (ML) models serving in future networks including IMT-2020, i.
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ITU-T Y.3531 - Cloud computing - Functional requirements for machine learning as a service
ProceduralUploaded on Jul 2, 2024This Recommendation provides system context, functional requirements and use cases for machine learning as a service (MLaaS).
Objective(s)
BS PAS 11281:2018 - Connected automotive ecosystems. Impact of security on safety. Code of practice
ProceduralUploaded on Jul 2, 2024PAS 11281 is the international standard on road vehicles that gives recommendations for managing security risks that might lead to a compromise of safety in a connected automotive ecosystem.
Objective(s)
ETSI EG 203341 V 1.1.1 - Core Network and Interoperability Testing (INT) - Approaches for Testing Adaptive Networks
ProceduralUploaded on Jul 2, 2024Methodology extending current experiences on the characteristics of 'adaptive networks' such as virtualization, self-organization, self-configuration, self-optimization, self-healing and self-learning offer huge advantages in future networks.
Objective(s)
ETSI GR ENI 009 V 1.1.1 - Experiential Networked Intelligence (ENI) - Definition of data processing mechanisms
ProceduralUploaded on Jul 2, 2024Effective data management and operation is extremely important. This work item is purposed to draft a GR of data operation requirements and mechanisms to better serve ENI system.
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ITU-T M.3080 - Framework of artificial intelligence enhanced telecom operation and management (AITOM)
ProceduralUploaded on Jul 2, 2024This Recommendation provides a framework for artificial intelligence (AI) enhanced telecom operation and management (AITOM).
Objective(s)
ITU-T Y.3178 - Functional framework of artificial intelligence-based network service provisioning in future networks including IMT-2020
ProceduralUploaded on Jul 2, 2024This Recommendation specifies a functional framework for network service provisioning based on artificial intelligence (AI) in future networks, including international mobile telecommunication-2020 (IMT-2020).
Objective(s)
IEEE 2830-2021 - IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning
ProceduralUploaded on Jul 2, 2024This standard defines a framework and architectures for machine learning in which a model is trained using encrypted data that has been aggregated from multiple sources and is processed by a third party trusted execution environment (TEE).
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ISO/IEC TR 24741:2018 - Information technology - Biometrics - Overview and application
ProceduralUploaded on Jul 2, 2024This document describes the history of biometrics and what biometrics does, the various biometric technologies in general use today.
Objective(s)
BS EN ISO/IEC 15067-3-51 - Information technology. Home Electronic System (HES) application model. Part 51. Framework of a Protected On-Premises Narrow AI Engine for an Energy Management System using Energy Management Agents (EMAs)
ProceduralUploaded on Jul 2, 2024This standard specifies a framework for adding artificial intelligence (AI) functions to support the energy management agent (EMA) specified in ISO/IEC for EMAs located on customer premises.
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ITU-T Y Supplement 55 - ITU-T Y.3170-series - Machine learning in future networks including IMT-2020: use cases
ProceduralUploaded on Jul 2, 2024This Supplement analyses use cases for machine learning in future networks including IMT-2020, and presents them in a unified format.
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ITU-T Y Supplement 70 - ITU-T Y.3800-series - Quantum key distribution networks - Applications of machine learning
ProceduralUploaded on Jul 2, 2024This Supplement presents the applications of machine learning (ML) in quantum key distribution networks (QKDNs).
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ITU-T Y.3176 - Machine learning marketplace integration in future networks including IMT-2020
ProceduralUploaded on Jul 2, 2024This Recommendation provides high-level requirements and the architecture for integration of ML marketplaces in future networks including IMT-2020.
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